{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The CIFAR-100 dataset: download and pre-processing\n",
    "============================\n",
    "\n",
    "The CIFAR-100 dataset can be downloaded at [www.cs.toronto.edu/~kriz/cifar.html](https://www.cs.toronto.edu/~kriz/cifar.html) and it is a single compressed file containing serialized numpy arrays. You can also download the dataset directly from the following link:\n",
    "\n",
    "- [cifar-100-python.tar.gz](https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz): test and training sets (161 MB)\n",
    "\n",
    "The archive contains three files: *meta*, *test*, *train*. Each of these files is a Python \"pickled\" object produced with cPickle. The pickle files can be easilly deserialized using the following function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def unpickle(file):\n",
    "    import cPickle\n",
    "    with open(file, 'rb') as fo:\n",
    "        dict = cPickle.load(fo)\n",
    "    return dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The unpickle() method reuturns a dictionary of elements. Using `unpickle()` on the file *meta* we obtain:\n",
    "\n",
    "**fine_label_names**: a 100 element list which gives meaningful names to the numeric labels in the labels array described above. For example, fine_label_names[0] == \"apple\", fine_label_names[1] == \"aquarium_fish\", fine_label_names[99] == \"worm\", etc.\n",
    "\n",
    "**coarse_label_names**: a 20 superclasses list. coarse_label_names[0] == \"aquatic_mammals\",  coarse_label_names[19] == \"vehicles_2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "my_dict = unpickle(\"./meta\")\n",
    "fine_label_names = my_dict[\"fine_label_names\"]\n",
    "coarse_label_names = my_dict[\"coarse_label_names\"]\n",
    "print(\"Fine labels: \")\n",
    "print fine_label_names\n",
    "print(\"\\n Coarse labels: \")\n",
    "print coarse_label_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *train* file contain the following arrays:\n",
    "\n",
    "**data**: numpy array (shape: 50000x3072) of uint8s. Each row of the array stores a 32x32 colour image. The first 1024 entries contain the red channel values, the next 1024 the green, and the final 1024 the blue. The image is stored in row-major order, so that the first 32 entries of the array are the red channel values of the first row of the image.\n",
    "\n",
    "**labels**: list of 50000 numbers in the range 0-99. The number at index i indicates the label of the ith image in the array data.\n",
    "\n",
    "To continue this tutorial you need to open the CIFAR-100 archive, extract the files and copy them into the same folder of this notebook. Once you did this you can proceed with the next step:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "my_dict = unpickle(\"./train\")\n",
    "for key, value in my_dict.items() :\n",
    "    print (key)\n",
    "\n",
    "print(\"\\nData shape:\")\n",
    "data = my_dict[\"data\"]\n",
    "print(data.shape)\n",
    "\n",
    "print(\"\\nLabels shape:\")\n",
    "fine_labels = my_dict[\"fine_labels\"]\n",
    "print(len(fine_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pre-processing in Numpy\n",
    "----------------------------\n",
    "\n",
    "Using **numpy** and **matplotlib** it is possible to pick a random image from the training set and show it. In this example I will take the RGB channels and I will reshape and stack them, then through matplotlib I will display the resulting image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "training_set = my_dict[\"data\"]\n",
    "random_int = np.random.randint(0,50000)\n",
    "image_array = training_set[random_int]\n",
    "image_R = np.reshape(image_array[0:1024], (32,32))\n",
    "image_G = np.reshape(image_array[1024:2048], (32,32))\n",
    "image_B = np.reshape(image_array[2048:4096], (32,32))\n",
    "image = np.dstack((image_R, image_G, image_B))\n",
    "\n",
    "fine_labels = my_dict[\"fine_labels\"][random_int]\n",
    "fine_label_name = fine_label_names[fine_labels]\n",
    "\n",
    "print fine_label_name\n",
    "plt.imshow(image)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pre-processing in Tensorflow\n",
    "---------------------------------\n",
    "\n",
    "In **tensorflow** we can easily manage the dataset. If all of your input data fit in memory (like in this case), the simplest way to create a dataset from them is to convert them to `tf.Tensor` objects and use `Dataset.from_tensor_slices()`. A `tf.data.Dataset` represents a sequence of elements, in which each element contains one or more Tensor objects. For example, in an image-based pipeline, an element might be a single training example, with a pair of tensors representing the image and a label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "tf_dataset = tf.data.Dataset.from_tensor_slices((my_dict[\"data\"], my_dict[\"fine_labels\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using tensorflow it is also possible to apply a **map function** to the dataset. The map function allows us to sequentially reshape the images and to create one-hot-vector target from a single integer label. The map iterates all the elements stored into the dataset, and uses an external function to parse the data. Into the parse function it can be possible to manipulate the image (e.g. standardisation, rotation, saturation, etc) using the `tf.image` utilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def _parse_function(image_array, label):\n",
    "    label = tf.cast(label, tf.int32)\n",
    "    image_R = tf.reshape(image_array[0:1024], [32, 32])\n",
    "    image_G = tf.reshape(image_array[1024:2048], [32, 32])\n",
    "    image_B = tf.reshape(image_array[2048:4096], [32, 32])\n",
    "    image = tf.stack([image_R, image_G, image_B], axis=2)\n",
    "    #Here it may be possible to apply image manipulation\n",
    "    #Uncomment and return if you want one-hot encoding\n",
    "    #label_one_hot = tf.one_hot(label, depth=10)\n",
    "    return image, label\n",
    "\n",
    "tf_dataset = tf_dataset.map(_parse_function) #parsing all the elements\n",
    "tf_dataset = tf_dataset.shuffle(50000) #shuffling all the elements\n",
    "print(tf_dataset.output_types)\n",
    "print(tf_dataset.output_shapes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have built a `Dataset` to represent your input data, the next step is to create an `Iterator` to access elements from that dataset. The `tf.data` API currently supports the following iterators, in increasing level of sophistication:\n",
    "\n",
    "- one-shot\n",
    "- initializable\n",
    "- reinitializable\n",
    "- feedable\n",
    "\n",
    "A **one-shot** iterator is the simplest form of iterator, which only supports iterating once through a dataset, with no need for explicit initialization. One-shot iterators handle almost all of the cases that the existing queue-based input pipelines support. I can be initialized using the method `make_one_shot_iterator()`. The iterator can be combined with the `batch()` method in order to get a **batch** of elements that we can use to feed the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "tf_batch = tf_dataset.batch(batch_size)\n",
    "print(tf_batch.output_types)\n",
    "print(tf_batch.output_shapes)\n",
    "\n",
    "#the iterator is created, it will take batch_size elements from the dataset\n",
    "iterator = tf_batch.make_one_shot_iterator()\n",
    "#every time next_element is called batch_size new elements are taken\n",
    "next_element = iterator.get_next()\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    #print(sess.run(next_element))\n",
    "    #print(sess.run(next_element))\n",
    "    features, labels = sess.run(next_element) #this is how you can get the features and labels back"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just to be sure we did not mess up with the dataset after all the previous operation, we can display a random image taken from the batch and check if it is corrupted. We can also print the associated label to see if it matches:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "random_int = np.random.randint(batch_size)\n",
    "image = features[random_int,:,:,:]\n",
    "label = labels[random_int]\n",
    "label_name = fine_label_names[label]\n",
    "print(label)\n",
    "print(label_name)\n",
    "plt.imshow(image)\n",
    "plt.show()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Complete pipeline in Tensorflow\n",
    "-------------------------------------\n",
    "\n",
    "Now it is time to stich everything togheter and create a tensorflow pipeline to load the entire CIFAR-100 dataset and use it for training a model. This session is separated from the previous parts and can run independently from other cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "def unpickle(file):\n",
    "    import cPickle\n",
    "    with open(file, 'rb') as fo:\n",
    "        dict = cPickle.load(fo)\n",
    "    return dict\n",
    "\n",
    "def _parse_function(image_array, label):\n",
    "    label = tf.cast(label, tf.int32)\n",
    "    image_R = tf.reshape(image_array[0:1024], [32, 32])\n",
    "    image_G = tf.reshape(image_array[1024:2048], [32, 32])\n",
    "    image_B = tf.reshape(image_array[2048:4096], [32, 32])\n",
    "    image_stack = tf.stack([image_R, image_G, image_B], axis=2)\n",
    "    #Here the image is standardised through tf.image\n",
    "    image = tf.image.per_image_standardization(image_stack)\n",
    "    #label_one_hot = tf.one_hot(label, depth=10)\n",
    "    return image, label\n",
    "\n",
    "#Load the test set\n",
    "print \"Processing test set...\"\n",
    "my_dict = unpickle(\"./test\")\n",
    "tf_test_dataset = tf.data.Dataset.from_tensor_slices((my_dict[\"data\"], my_dict[\"fine_labels\"]))\n",
    "#Concatenate all the 5 batches in a single training dataset\n",
    "print \"Processing train set...\"\n",
    "my_dict = unpickle(\"./train\")\n",
    "tf_train_dataset = tf.data.Dataset.from_tensor_slices((my_dict[\"data\"], my_dict[\"fine_labels\"]))\n",
    "\n",
    "#Parsing all the elements\n",
    "print \"Parsing the datasets...\"\n",
    "tf_test_dataset = tf_test_dataset.map(_parse_function)\n",
    "tf_train_dataset = tf_train_dataset.map(_parse_function) \n",
    "\n",
    "#Shuffling the test set\n",
    "print \"Shuffling test dataset...\"\n",
    "tf_test_dataset = tf_test_dataset.shuffle(buffer_size=50000)\n",
    "print \"done!\"\n",
    "\n",
    "#Printing type and shape of the dataset\n",
    "print \"Test dataset:\"\n",
    "print(tf_test_dataset.output_types)\n",
    "print(tf_test_dataset.output_shapes)\n",
    "print \"Train dataset:\"\n",
    "print(tf_train_dataset.output_types)\n",
    "print(tf_train_dataset.output_shapes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have **two datasets**, one for training and one for testing. You should notice that in the `_parse_function()` I applied a `tf.image.per_image_standardization()` that produced normalised `tf.float32` matrices. The **standardisation** linearly scales image to have zero mean and unit norm. It is important to use a per-image normalisation instead of a batch or dataset standardisation. If the images are standardised using information from a large set we are leaking information into our dataset. Once the model is trained we won't be able to normalize over all of our future data points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with tf.name_scope('dataset'):\n",
    "    batch_size = 32\n",
    "    num_epochs = 5\n",
    "    tf_train_dataset = tf_train_dataset.batch(batch_size)\n",
    "    tf_train_dataset = tf_train_dataset.repeat(num_epochs)\n",
    "    iterator = tf_train_dataset.make_one_shot_iterator()\n",
    "    next_batch_imgs, next_batch_labels = iterator.get_next()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have done with the graph and we can initialize a session to sample batches and use them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess = tf.Session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the training dataset declaring an iterator. Then we can use the session and get the features and labels of a batch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "features, labels = sess.run([next_batch_imgs, next_batch_labels])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Attention: after the standardisation of the images, we will get problems to visualise them with Matplotlib and Numpy. Now the images are normalised `float32` and no more `uint8` for this reason you will see something wired if you try to plot the features (try the code below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "my_dict = unpickle(\"./meta\")\n",
    "labels_names = my_dict[\"fine_label_names\"]\n",
    "#random_int = np.random.randint(batch_size)\n",
    "image = features[0,:,:,:]\n",
    "label = labels[0]\n",
    "label_name = labels_names[label]\n",
    "print features.shape\n",
    "print label_name\n",
    "plt.imshow(image)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To solve the issue we can directly use the **tensorboard** image summary. It is very easy to write the images in the summary thanks to the `tf.summary.image()` method. This method takes as input an image (or a batch of images) and display them in the `image` tab of tensorboard.\n",
    "\n",
    "The code below save a summary in `/tmp/log/`. To visualise the summary it is necessary to start tensorboard. From terminal run the command `tensorboard --logdir=/tmp/log/` and then open the browser to the address written in console. If everything worked correctly you should see some images under the tab `IMAGE`. You can resize them using the checkbox in the top-left corner `Show actual image size`.\n",
    "\n",
    "First of all we declare a placeholder that will store the features returned by a previous call to `iterator.get_next()`. We need a placeholder because calling the iterator directly through a summary operation would make the batch move forward of one step. Secondly, we create a summary operation that takes the content of the placeholder. The `max_output` parameter sets the number of images that will be displayed from the batch (default is 3). Thirdly, we have to create a summary writer in order to save on our system the tensorflow graph. To make the graph more clear I put everything inside a name scope."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with tf.name_scope('batch_visualisation'):\n",
    "    batch_placeholder = tf.placeholder(tf.float32, shape=(batch_size, 32, 32, 3))\n",
    "    summary_op = tf.summary.image(\"batch_images\", batch_placeholder, max_outputs=batch_size)\n",
    "    writer = tf.summary.FileWriter(\"/tmp/log/\", sess.graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we can call the summary operation that will take the latest images in the batch and will write them in the summary. You can repeat the following snippet multimple times, but in order to see new images you have to ask for a new batch using `features, labels = sess.run([next_batch_imgs, next_batch_labels])`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "summary = sess.run(summary_op, feed_dict={batch_placeholder: features})\n",
    "writer.add_summary(summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pre-processing using TFRecords\n",
    "---------------------------------------\n",
    "\n",
    "Another approach is to convert whatever data you have into a supported format. This approach makes it easier to mix and match data sets and network architectures. The recommended format for TensorFlow is a **TFRecords** file. You write a little program that gets your data, stuffs it in an Example protocol buffer, serializes the protocol buffer to a string, and then writes the string to a TFRecords file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Couldn't import dot_parser, loading of dot files will not be possible.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import cPickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def pickle_to_tfrecord(pickle_list, output_file):\n",
    "    with tf.python_io.TFRecordWriter(output_file) as record_writer:\n",
    "        for input_file in pickle_list:\n",
    "            #Opening the pickle (return a dictionary)\n",
    "            with open(input_file, 'rb') as fo:\n",
    "                data_dict = cPickle.load(fo)\n",
    "            #Store the dictionary data and labels in two variables\n",
    "            data = data_dict['data']\n",
    "            labels = data_dict['fine_labels']\n",
    "            #Each row of data is an image (each element is a byte)\n",
    "            #Each row of labels is an integer (the single element is an int64)\n",
    "            for i in range(len(labels)):\n",
    "                #Getting the data as train feature\n",
    "                bytes_feature = tf.train.Feature(bytes_list=tf.train.BytesList(value=[data[i].tobytes()]))\n",
    "                int64_feature = tf.train.Feature(int64_list=tf.train.Int64List(value=[labels[i]]))\n",
    "                #Stuff the data in an Example buffer\n",
    "                example = tf.train.Example(features=tf.train.Features(feature={'image': bytes_feature,\n",
    "                                                                               'label': int64_feature}))\n",
    "                #Serialize example to string and write in tfrecords\n",
    "                record_writer.write(example.SerializeToString())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use the function to read the CIFAR-10 pickle files and store them in a TFRecords file. I will do it two times, one for the training set, the other for the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pickle_to_tfrecord([\"./train\"],\n",
    "                  \"./cifar100_train.tfrecord\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pickle_to_tfrecord([\"./test\"], \"./cifar100_test.tfrecord\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having the two TFRecords file we can use again the utilities of `tf.data` and in particular the `TFRecordDataset` class. We can parse the data and recover the `uint8` image from the serialized `string` using the method `tf.decode_raw()`. We apply standardisation as usual and we get the one-hot vector from the label integer class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading the datasets...\n",
      "Parsing the datasets...\n",
      "Verifying types and shapes...\n",
      "(tf.float32, tf.int64)\n",
      "(TensorShape([Dimension(32), Dimension(32), Dimension(3)]), TensorShape([]))\n"
     ]
    }
   ],
   "source": [
    "def _parse_function(example_proto):\n",
    "    features = {\"image\": tf.FixedLenFeature((), tf.string, default_value=\"\"),\n",
    "               \"label\": tf.FixedLenFeature((), tf.int64, default_value=0)}\n",
    "    parsed_features = tf.parse_single_example(example_proto, features)\n",
    "    image_decoded = tf.decode_raw(parsed_features[\"image\"], tf.uint8) #char -> uint8\n",
    "    image_R = tf.reshape(image_decoded[0:1024], [32, 32])\n",
    "    image_G = tf.reshape(image_decoded[1024:2048], [32, 32])\n",
    "    image_B = tf.reshape(image_decoded[2048:4096], [32, 32])\n",
    "    image_reshaped = tf.reshape(image_decoded, [32, 32, 3])\n",
    "    image_stack = tf.stack([image_R, image_G, image_B], axis=2)\n",
    "    image = tf.image.per_image_standardization(image_stack)\n",
    "    #label_one_hot = tf.one_hot(parsed_features[\"label\"], depth=10)\n",
    "    label = parsed_features[\"label\"]\n",
    "    return image, label\n",
    "\n",
    "print \"Loading the datasets...\"\n",
    "tf_train_dataset = tf.data.TFRecordDataset(\"./cifar100_train.tfrecord\")\n",
    "print \"Parsing the datasets...\"\n",
    "tf_train_dataset = tf_train_dataset.map(_parse_function)\n",
    "print \"Verifying types and shapes...\"\n",
    "print(tf_train_dataset.output_types)\n",
    "print(tf_train_dataset.output_shapes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.name_scope('dataset'):\n",
    "    batch_size = 32\n",
    "    num_epochs = 5\n",
    "    tf_train_dataset = tf_train_dataset.batch(batch_size)\n",
    "    tf_train_dataset = tf_train_dataset.repeat(num_epochs)\n",
    "    iterator = tf_train_dataset.make_one_shot_iterator()\n",
    "    next_batch_imgs, next_batch_labels = iterator.get_next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess = tf.Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "features, labels = sess.run([next_batch_imgs, next_batch_labels])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19\n",
      "(32, 32, 32, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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7OCFfnOCLCp8X+PzM+CSEx4B7EkwDWkfK6UB63JNaI2kjt0bWRlaltErVRs26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\nWWPvGuhO0WlIO7mGmRr11JClsW4zy2JY18CyHlnX9yzrB7b1A36o+h43I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Yh0BAMCA6WT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4f41d9fdd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "image = features[0,:,:,:]\n",
    "label = labels[0]\n",
    "print(label)\n",
    "print(features.shape)\n",
    "plt.imshow(image)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
